Neural correlates of post-traumatic brain injury (TBI) attention deficits in children

Traumatic brain injury (TBI) in children is a major public health concern worldwide. Attention deficits are among the most common neurocognitive and behavioral consequences in children post-TBI which have significant negative impacts on their educational and social outcomes and compromise the quality of their lives. However, there is a paucity of evidence to guide the optimal treatment strategies of attention deficit related symptoms in children post-TBI due to the lack of understanding regarding its neurobiological substrate. Thus, it is critical to understand the neural mechanisms associated with TBI-induced attention deficits in children so that more refined and tailored strategies can be developed for diagnoses and long-term treatments and interventions. This dissertation is the first study to investigate neurobiological substrates associated with post-TBI attention deficits in children using both anatomical and functional neuroimaging data. The goals of this project are to discover the quantitatively measurable markers utilizing diffusion tensor imaging (DTI), structural magnetic resonance imaging (MRI), and functional MRI (fMRI) techniques, and to further identify the most robust neuroimaging features in predicting severe post-TBI attention deficits in children, by utilizing machine learning and deep learning techniques. A total of 53 children with TBI and 55 controls from age 9 to 17 are recruited. The results show that the systems-level topological properties in left frontal regions, parietal regions, and medial occipitotemporal regions in structural and functional brain network are significantly associated with inattentive and/or hyperactive/impulsive symptoms in children post-TBI. Semi-supervised deep learning modeling further confirms the significant contributions of these brain features in the prediction of elevated attention deficits in children post-TBI. The findings of this project provide valuable foundations for future research on developing neural markers for TBI-induced attention deficits in children, which may significantly assist the development of more effective and individualized diagnostic and treatment strategies

Understanding cognitive dysfunction in secondary progressive multiple sclerosis using functional and structural MRI

This thesis concerns a 2 year follow-up study of people with secondary progressive multiple sclerosis (SPMS). I investigate: (1) cognitive performance of SPMS and changes over time, (2) the classification of cognitive impairment and predictors of this, (3) mechanisms underlying the SPMS phenotype with and without cognitive impairment using functional and structural MRI. The literature has highlighted the input of executive dysfunction in the cognitive profile of SPMS over and above that seen in other multiple sclerosis (MS) phenotypes. I looked at cognitive performance in SPMS, and predictors of this in this pure SPMS cohort study. I found that being employed, having higher IQ, more premorbid leisure interests, and higher qualifications mitigate against negative cognitive outcomes in SPMS. Additionally, anxiety, even when not reaching clinically diagnostic levels, impacts on tests of information processing speed, verbal working memory, and executive function in SPMS. The symbol digit modality test (SDMT) at baseline is predicted by MS lower limb disability outcome measures; the Expanded Disability Status Scale (EDSS) and timed 25 foot walk (T25FW) which emphasises the role of the SDMT as an adjunctive measure of clinical disability prediction in studies. I show that decline on the SDMT at follow-up is purely predicted by cognitive measures of information processing speed and working memory at either timepoint, supporting, and furthering, the evidence for the SDMT as a sentinel assessment of cognitive performance in SPMS. These findings inform future longitudinal cognitive studies in SPMS, particularly with regards to the importance of tests of executive function, and important associations with clinical outcomes in a highly disabled cohort. I also considered the threshold for classifying cognitive impairment, and its implications. There is marked heterogeneity in these thresholds due to the lack of current consensus on a diagnostic criteria. Using a higher threshold for cognitive impairment in my studies strengthened the associations with clinically relevant outcomes. Additionally, unemployment showed the greatest association with cognitive impairment regardless of criteria used. I found that assessments of information processing speed, verbal memory, and executive function had the greatest input to cognitive impairment in SPMS. These findings indicate the importance of these cognitive domains and demographic factors when evaluating cognitive status in SPMS. These results will guide the international consensus on how best to measure cognitive impairment in SPMS, and in MS more broadly. Posterior and deep resting state networks (RSNs) have been shown to be altered in resting state functional MRI (rs-fMRI) studies of progressive MS phenotypes. I confirm this using functional connectivity (FC) and highlight that this is mainly in terms of cognitive RSNs in SPMS versus healthy controls using a global rs-fMRI analysis technique. Additionally, with cognitive impairment in SPMS, I show that there are key attentional RSN FC reductions. I further highlight the importance of more stringent classification criteria of cognitive impairment to allow for more detailed evaluation of dynamic FC changes, that are missed when using a lenient criteria. Over time, the development of cognitive impairment in SPMS from a preserved state appears to relate to reduced FC in working memory, posterior default mode (DMN) and visual RSNs, and increased FC in the executive control, and more anterior DMN hubs at baseline. Therefore, alterations in posterior cognitive and executive RSNs may inform cognitive status in SPMS. These results provide, to my knowledge, the first longitudinal rs-fMRI study of cognitive status in SPMS. Regional grey matter atrophy has been shown to be greater in SPMS then in other MS phenotypes. I found that SPMS cognitive impairment is associated with grey matter volume, cortical grey matter volume, and deep grey matter and regional deep grey matter atrophy. I also highlighted that proportionally, within the cerebellum, there are greater percentage changes in FC versus volume in those with SPMS with cognitive impairment versus in SPMS overall. These findings therefore show the importance of deeper grey matter atrophy in SPMS underlying cognitive impairment, and indicate the need for a longitudinal study of rs-fMRI and regional grey matter MRI metrics to understand the interplay of underlying mechanisms in more detail

The role of MRI in diagnosing autism: a machine learning perspective.

There is approximately 1 in every 44 children in the United States suffers from autism spectrum disorder (ASD), a disorder characterized by social and behavioral impairments. Communication difficulties, interpersonal difficulties, and behavioral difficulties are the top common symptoms. Even though symptoms can begin as early as infancy, it may take multiple visits to a pediatric specialist before an accurate diagnosis can be made. In addition, the diagnosis can be subjective, and different specialists may give different scores. There is a growing body of research suggesting differences in brain development and/or environmental and/or genetic factors contribute to autism development, but scientists have yet to identify exactly the pathology of this disorder. ASD can currently be diagnosed by a set of diagnostic evaluations, regarded as the gold standard, such as the Autism Diagnostic Observation Schedule (ADOS) or the Autism Diagnostic Interview-Revised (ADI-R). A team of qualified clinicians is needed for performing the behavioral and communication tests as well as clinical history information, hence a considerable amount of time, effort, and subjective judgment is involved in using these gold-standard diagnostic instruments. In addition to standard observational assessment, recent advancements in neuroimaging and machine learning suggest a rapid and objective alternative, using brain imaging. An investigation of the employment of different imaging modalities, namely Diffusion Tensor Imaging (DTI), and resting state functional MRI (rs-fMRI) for autism diagnosis is presented in this comprehensive work. A detailed study of the implementation of feature engineering tools to find discriminant insights from different brain imaging modalities, including the use of novel feature representations, and the use of a machine learning framework to assist in the accurate classification of autistic individuals is introduced in this dissertation. Based on three large publicly available datasets, this extensive research highlights different decisions along the pipeline and their impact on diagnostic accuracy. It also identifies potentially impacted brain regions that contribute to an autism diagnosis. Achieving high global state-of-the-art cross-validated accuracy confirms the benefits of feature representation and feature engineering in extracting useful information, as well as the potential benefits of utilizing neuroimaging in the diagnosis of autism. This should enable an early, automated, and more objective personalized diagnosis

Auto-ASD-Network: A technique based on Deep Learning and Support Vector Machines for diagnosing Autism Spectrum Disorder using fMRI data

Quantitative analysis of brain disorders such as Autism Spectrum Disorder (ASD) is an ongoing field of research. Machine learning and deep learning techniques have been playing an important role in automating the diagnosis of brain disorders by extracting discriminative features from the brain data. In this study, we propose a model called Auto-ASD-Network in order to classify subjects with Autism disorder from healthy subjects using only fMRI data. Our model consists of a multilayer perceptron (MLP) with two hidden layers. We use an algorithm called SMOTE for performing data augmentation in order to generate artificial data and avoid overfitting, which helps increase the classification accuracy. We further investigate the discriminative power of features extracted using MLP by feeding them to an SVM classifier. In order to optimize the hyperparameters of SVM, we use a technique called Auto Tune Models (ATM) which searches over the hyperparameter space to find the best values of SVM hyperparameters. Our model achieves more than 70% classification accuracy for 4 fMRI datasets with the highest accuracy of 80%. It improves the performance of SVM by 26%, the stand-alone MLP by 16% and the state of the art method in ASD classification by 14%. The implemented code will be available as GPL license on GitHub portal of our lab (https://github.com/PCDS)

A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification

Recent applications of pattern recognition techniques on brain connectome classification using functional connectivity (FC) neglect the non-Euclidean topology and causal dynamics of brain connectivity across time. In this paper, a deep probabilistic spatiotemporal framework developed based on variational Bayes (DSVB) is proposed to learn time-varying topological structures in dynamic brain FC networks for autism spectrum disorder (ASD) identification. The proposed framework incorporates a spatial-aware recurrent neural network to capture rich spatiotemporal patterns across dynamic FC networks, followed by a fully-connected neural network to exploit these learned patterns for subject-level classification. To overcome model overfitting on limited training datasets, an adversarial training strategy is introduced to learn graph embedding models that generalize well to unseen brain networks. Evaluation on the ABIDE resting-state functional magnetic resonance imaging dataset shows that our proposed framework significantly outperformed state-of-the-art methods in identifying ASD. Dynamic FC analyses with DSVB learned embeddings reveal apparent group difference between ASD and healthy controls in network profiles and switching dynamics of brain states

A meta-analysis of machine learning classification tools using rs-fmri data for autism spectrum disorder diagnosis

The Autism Spectrum Disorder (ASD) is a complex and heterogeneous neurodevelopmental condition characterized by cognitive, behavioral, and social dysfunction. Much effort is being made to identify brain imaging biomarkers and develop tools that could facilitate its diagnosis - currently based on behavioral criteria through a lengthy and timeconsuming process. In particular, the use of Machine Learning (ML) classifiers based on resting-state functional Magnetic Resonance Imaging (rs-fMRI) data is promising, but there is an ongoing need for further research on their accuracy. Therefore, we conducted a systematic review and meta-analysis to summarize and aggregate the available evidence in the literature so far. The systematic search resulted in the selection of 93 articles, whose data were extracted and analyzed through the systematic review. A bivariate randomeffects meta-analytic model was implemented to investigate the sensitivity and specificity across the 55 studies (132 independent samples) that offered sufficient information for a quantitative analysis. Our results indicated overall summary sensitivity and specificity estimates of 73.8% (95% CI: 71.8-75.8%) and 74.8% (95% CI: 72.3-77.1%), respectively, and Support Vector Machine (SVM) stood out as the most used classifier, presenting summary estimates above 76%. Studies with bigger samples tended to obtain worse accuracies, except in the subgroup analysis for Artificial Neural Network (ANN) classifiers. The use of other brain imaging or phenotypic data to complement rs-fMRI information seem to be promising, achieving specially higher sensitivities (p = 0.002) when compared to rs-fMRI data alone (84.7% - 95% CI: 78.5-89.4% - versus 72.8% - 95% CI: 70.6-74.8%). Lower values of sensitivity/specificity were found when the number of Regions of Interest (ROIs) increased. We also highlight the performance of the approaches using the Automated Anatomical Labelling atlas with 116 ROIs (AAL116). Regarding the features used to train the classifiers, we found better results using the Pearson Correlation (PC) Fishertransformed or other features in comparison to the use of the PC without modifications. Finally, our analysis showed AUC values between acceptable and excellent, but given the many limitations indicated in our study, further well-designed studies are warranted to extend the potential use of those classification algorithms to clinical settings.Agência 1O Transtorno do Espectro Autista (TEA) é uma condição complexa e heterogênea que afeta o desenvolvimento cerebral e é caracterizada por disfunções cognitivas, comportamentais e sociais. Muito esforço vem sendo feito para identificar biomarcadores baseados em imagens cerebrais e desenvolver ferramentas que poderiam facilitar o diagnóstico do TEA - atualmente baseado em critérios comportamentais, através de um processo longo e demorado. Em particular, o uso de algoritmos de Aprendizado de Máquina para classificação de dados de Imagens de Ressonância Magnética funcional em estado de repouso (rs-fMRI) é promissor, mas há uma necessidade contínua de pesquisas adicionais a respeito da precisão desses classificadores. Assim, este trabalho realiza uma revisão sistemática e meta-análise de modo a resumir e agregar as evidências disponíveis na literatura da área até o momento. A busca sistemática por artigos resultou na seleção de 93 deles, que tiveram seus dados extraídos e analisados através da revisão sistemática. Um modelo meta-analítico bivariado de efeitos aleatórios foi implementado para investigar a sensibilidade e especificidade dos 55 estudos (132 amostras independentes) que ofereceram informação suficiente para serem utilizados na análise quantitativa. Os resultados obtidos indicaram estimativas gerais de sensibilidade e especificidade de 73.8% (95% IC: 71.8- 75.8%) e 74.8% (95% IC: 72.3-77.1%), respectivamente, e os classificadores baseados em SVM (do inglês, Support Vector Machine) se destacaram como os mais utilizados, apresentando estimativas acima de 76%. Estudos que utilizaram amostras maiores tenderam a obter piores resultados de precisão, com exceção do subgrupo composto por classificadores baseados em Redes Neurais Artificiais. O uso de outros tipos de imagens cerebrais ou dados fenotípicos para complementar as informações obtidas através da rs-fMRI se mostrou promissor, alcançando especialmente sensibilidades mais altas ( = 0.002) em relação aos estudos que utilizaram apenas dados de rs-fMRI (84.7% - 95% IC: 78.5-89.4% - versus 72.8% - 95% IC: 70.6-74.8%). Valores menores de sensibilidade/especificidade foram encontrados quando o número de Regiões de Interesse (ROIs, do inglês Regions of Interest) aumentou. Vale destacar também o desempenho das abordagens utilizando o atlas AAL (do inglês, Automated Anatomical Labelling) com 116 ROIs. Em relação às features usadas para treinar os classificadores, foram encontrados melhores resultados nos estudos que utilizaram a correlação de Pearson em conjunto com a transformação Z de Fisher ou outras features em comparação ao uso da correlação de Pearson sem modifica- ções. Finalmente, a análise revelou valores da área sob a curva ROC (do inglês, Receiver Operating Characteristic) entre aceitável e excelente. Entretanto, considerando as várias limitações que são indicadas no estudo, mais estudos bem desenhados são necessários para estender o uso potencial desses algoritmos de classificação a ambientes clínicos

Contributions to the study of Austism Spectrum Brain conectivity

164 p.Autism Spectrum Disorder (ASD) is a largely prevalent neurodevelopmental condition with a big social and economical impact affecting the entire life of families. There is an intense search for biomarkers that can be assessed as early as possible in order to initiate treatment and preparation of the family to deal with the challenges imposed by the condition. Brain imaging biomarkers have special interest. Specifically, functional connectivity data extracted from resting state functional magnetic resonance imaging (rs-fMRI) should allow to detect brain connectivity alterations. Machine learning pipelines encompass the estimation of the functional connectivity matrix from brain parcellations, feature extraction and building classification models for ASD prediction. The works reported in the literature are very heterogeneous from the computational and methodological point of view. In this Thesis we carry out a comprehensive computational exploration of the impact of the choices involved while building these machine learning pipelines

FCNet: A Convolutional Neural Network for Calculating Functional Connectivity from functional MRI

Investigation of functional brain connectivity patterns using functional MRI has received significant interest in the neuroimaging domain. Brain functional connectivity alterations have widely been exploited for diagnosis and prediction of various brain disorders. Over the last several years, the research community has made tremendous advancements in constructing brain functional connectivity from timeseries functional MRI signals using computational methods. However, even modern machine learning techniques rely on conventional correlation and distance measures as a basic step towards the calculation of the functional connectivity. Such measures might not be able to capture the latent characteristics of raw time-series signals. To overcome this shortcoming, we propose a novel convolutional neural network based model, FCNet, that extracts functional connectivity directly from raw fMRI time-series signals. The FCNet consists of a convolutional neural network that extracts features from time-series signals and a fully connected network that computes the similarity between the extracted features in a Siamese architecture. The functional connectivity computed using FCNet is combined with phenotypic information and used to classify individuals as healthy controls or neurological disorder subjects. Experimental results on the publicly available ADHD-200 dataset demonstrate that this innovative framework can improve classification accuracy, which indicates that the features learnt from FCNet have superior discriminative power

The white matter connectome as an individualized biomarker of language impairment in temporal lobe epilepsy.

ObjectiveThe distributed white matter network underlying language leads to difficulties in extracting clinically meaningful summaries of neural alterations leading to language impairment. Here we determine the predictive ability of the structural connectome (SC), compared with global measures of white matter tract microstructure and clinical data, to discriminate language impaired patients with temporal lobe epilepsy (TLE) from TLE patients without language impairment.MethodsT1- and diffusion-MRI, clinical variables (CVs), and neuropsychological measures of naming and verbal fluency were available for 82 TLE patients. Prediction of language impairment was performed using a robust tree-based classifier (XGBoost) for three models: (1) a CV-model which included demographic and epilepsy-related clinical features, (2) an atlas-based tract-model, including four frontotemporal white matter association tracts implicated in language (i.e., the bilateral arcuate fasciculus, inferior frontal occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus), and (3) a SC-model based on diffusion MRI. For the association tracts, mean fractional anisotropy was calculated as a measure of white matter microstructure for each tract using a diffusion tensor atlas (i.e., AtlasTrack). The SC-model used measurement of cortical-cortical connections arising from a temporal lobe subnetwork derived using probabilistic tractography. Dimensionality reduction of the SC was performed with principal components analysis (PCA). Each model was trained on 49 patients from one epilepsy center and tested on 33 patients from a different center (i.e., an independent dataset). Randomization was performed to test the stability of the results.ResultsThe SC-model yielded a greater area under the curve (AUC; .73) and accuracy (79%) compared to both the tract-model (AUC: .54, p < .001; accuracy: 70%, p < .001) and the CV-model (AUC: .59, p < .001; accuracy: 64%, p < .001). Within the SC-model, lateral temporal connections had the highest importance to model performance, including connections similar to language association tracts such as links between the superior temporal gyrus to pars opercularis. However, in addition to these connections many additional connections that were widely distributed, bilateral and interhemispheric in nature were identified as contributing to SC-model performance.ConclusionThe SC revealed a white matter network contributing to language impairment that was widely distributed, bilateral, and lateral temporal in nature. The distributed network underlying language may be why the SC-model has an advantage in identifying sub-components of the complex fiber networks most relevant for aspects of language performance